Abstract
Path inference is an essential component for many location based services. In this paper, we study the problem of inferring vehicle moving paths from noisy and incomplete data captured by GPS devices mounted on vehicles. We propose a collaborative filter model to incorporate both static and dynamic context information to achieve highly accurate path inference. A tensor decomposition technique is adopted to extract context-aware spatial and temporal features from the location data with minimal a prior information about the underlying roads such as the path lengths. We evaluated our framework using a large scale real world dataset, which has one-month location data from more than eight thousand taxis in Beijing. The evaluation results show that our method outperforms state-of-the-art techniques.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (Grant No. U1536107), Xinjiang Uygur Autonomous Region Science and Technology Project (Grant No. Y3V0021402), and the “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant No. XDA06040101).
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Wang, H., Wen, H., Yi, F., Li, Z., Sun, L. (2016). Tensor Filter: Collaborative Path Inference from GPS Snippets of Vehicles. In: Yang, Q., Yu, W., Challal, Y. (eds) Wireless Algorithms, Systems, and Applications. WASA 2016. Lecture Notes in Computer Science(), vol 9798. Springer, Cham. https://doi.org/10.1007/978-3-319-42836-9_10
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DOI: https://doi.org/10.1007/978-3-319-42836-9_10
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